Joint scheduling allows for improved throughput and coverage in wireless networks by coordinating (to one degree or another) the scheduling decisions of neighboring base stations (BSs). Specifically, BSs participating in joint scheduling may make user equipment (UE) scheduling decisions and/or modulation coding scheme (MCS) assignments that mitigate Inter-Cell-Interference (ICI) in neighboring cells based on scheduling information communicated by neighboring BSs. For instance, neighboring BSs may coordinate their scheduling so as to avoid scheduling respective cell edge UEs (CEUs) and/or assigning similar MCSs to the same time-frequency resource, thereby smoothing out interference over thermal noise levels (IoTs) through improved link adaptation and more accurate channel quality indicator (CQI) predication. Joint scheduling is particularly advantageous for managing ICI in the uplink communications channel, where IoTs tend to vary to greater degrees due to the persistent shifting of transmission points from resource block (RB) to RB. That is to say, scheduled UEs are distributed throughout the cell, which causes the effective uplink transmission point to migrate between successive RBs.
Conventional joint scheduling techniques generally involve a negotiation between neighboring BSs for purposes of reaching a coordinated scheduling agreement, and typically includes computation of the scheduling agreement using (for instance) an exhaustive search approach. However, the negotiation/computation required by conventional joint scheduling may consume substantial network resources, particularly in large networks. To reduce the network resources consumed by joint scheduling, BSs within a network may be sub-divided into two or more clusters of BSs, with separate joint scheduling (e.g., intra-cluster joint scheduling) being performed by the member BSs of each cluster. This clustering technique reduces the complexity of the scheduling/negotiation by reducing the number of BSs participating in a given instance of joint scheduling. Even with clustering, however, the negotiation/computation required by conventional joint scheduling may consume substantial amounts of network resources (e.g., backhaul bandwidth, computation capacity, etc.). As such, techniques extending beyond the basic clustering of BSs are desired for reducing network costs associated with joint scheduling.
Furthermore, conventional intra-cluster joint scheduling only considers ICI consequences for member BSs of the instant cluster, and consequently does not mitigate ICI levels observed by base stations in external clusters. As such, mechanisms and techniques for reducing inter-cluster interference are desired in an effort to further mitigate overall ICI levels experienced in the network.